LAUSR

Department of Chemistry, University of Cambridge, Cambridge, United Kingdom Department of Computer Science and Technology, Cambridge, United Kingdom Department of Computing, Imperial College London, London, United Kingdom Correspondence Salvatore Cardamone, University Chemical Laboratory, Lensfield Road, Cambridge CB2 1EW, United Kingdom. Email: [email protected] Funding information H2020 European Research Council, Grant/ Award Number: 671653; Royal Society, Grant/Award Numbers: RG140728, UF160398, UF110161; EU Horizon 2020, Grant/Award Number: 671653 Abstract Massively parallel architectures offer the potential to significantly accelerate an application relative to their serial counterparts. However, not all applications exhibit an adequate level of data and/or task parallelism to exploit such platforms. Furthermore, the power consumption associated with these forms of computation renders “scaling out” for exascale levels of performance incompatible with modern sustainable energy policies. In this work, we investigate the potential for field-programmable gate arrays (FPGAs) to feature in future exascale platforms, and their capacity to improve performance per unit power measurements for the purposes of scientific computing. We have focused our efforts on variational Monte Carlo, and report on the benefits of coprocessing with a FPGA relative to a purely multicore system.